1. Field
The following description relates to energy-aware cloud and clustering computing technologies in distribution environments, and particularly to profiling-based energy-aware recommendation apparatus and method.
2. Description of the Related Art
Cloud systems are recently popular due to the predominance of computing platforms. Such cloud systems are enlarged more than a service and an arrangement model, which are initially proposed. Thus, for adopting such cloud systems, the road map of cloud computing faces the challenges to overcome. The challenges to overcome indicate availability, performance, and scalability, which directly affect the platform energy.
Cloud computing requires a plurality of product machines to provide resources to clients without the time when the product machines do not operate and its performance degradation. A price setting model based on the utility is very cost-effective from the point of clients. However, from the point of cloud service providers, a big amount of the energy should be consumed so that infrastructures can support the availability and performance. In other words, such cloud platforms are made largely based on the large-scale facilities, i.e., clusters and data centers, resulting in the huge energy emissions. In 2006, the large-scale IT infrastructures in U.S. consumed 61 billion kWh of electricity, which costed 4.5 billion dollars. Also, it is estimated that in 2011, 7.4 billion dollars of energy was consumed, which almost doubled the energy consumption in 2006.
In addition, the data centers that consume such a large-scale energy affect badly on the environment because of the huge carbon emissions, resulting in the conclusions of various treaties, such as the Green Grid, the Green Computing Impact Organization (GCIO), and the Green Electronics Council. IT infrastructure development companies e.g., DELL, HP, Intel, AMD, IBM, and Lenovo are active members complying with such treaties. The purposes of such treaties are to propose techniques and technologies, which can contribute to reducing the energy costs of the IT infrastructure e.g., desktops in small-scale to data centers in large-scale. In conclusion, energy-effective schemes and techniques are applied to the manufacture of infrastructures and become the essential point in sales based on the IT infrastructures.
However, the energy consumption of the large-scale infrastructures is the result that is caused not from the use of physical infrastructures but from provisioning methods of such physical infrastructures. In such infrastructures, the use of sources and operation of applications play a major role in the energy consumption of the data centers. Thus, the scope of the energy efficiency varies from a macro level, e.g., data center, to a micro level, e.g., CPU utilization.
Among them, CPU is the most power hungry component among electronic elements installed in submachines that belongs to the micro level. However, the innovative technologies in microprocessor technologies, such as dynamic voltage and frequency scaling (DVFS) are recently developed to improve such CPU power efficiency. Particularly, multi-core processors are mainstreamed, where each core is used as a virtual CPU in a virtual machine.
However, such a platform does not focus on large-scale data and clusters because of the effects of cloud computing. The small organizations and laboratories that share the infrastructures are hosted to private clouds. Furthermore, unlike data centers and clusters, cloud platforms are also being hosted over heterogeneous environment where some machines are servers and some are desktops so as to provide a cloud platform. In addition, there has been the study using the micro structure with a low-cost cloud platform. However, even for the small-scale cloud platform that is low-cost and energy-efficient, if the provisioning is not performed appropriately, it would be inefficient in terms of the energy consumption.